Publications

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Selected filter tags (click to remove): Lukas-Schäfer

2022

Lukas Schäfer, Filippos Christianos, Josiah P. Hanna, Stefano V Albrecht
Decoupled Reinforcement Learning to Stabilise Intrinsically-Motivated Exploration
International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2022
Abstract | BibTex | arXiv | Code
AAMASdeep-rlintrinsic-reward

Lukas Schäfer
Task Generalisation in Multi-Agent Reinforcement Learning
International Conference on Autonomous Agents and Multiagent Systems, Doctoral Consortium (AAMAS), 2022
Abstract | BibTex | Paper
AAMASmulti-agent-rl

2021

Georgios Papoudakis, Filippos Christianos, Lukas Schäfer, Stefano V. Albrecht
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
Conference on Neural Information Processing Systems, Datasets and Benchmarks Track (NeurIPS), 2021
Abstract | BibTex | arXiv | Code
NeurIPSdeep-rlmulti-agent-rl

Rujie Zhong, Josiah P. Hanna, Lukas Schäfer, Stefano V. Albrecht
Robust On-Policy Data Collection for Data-Efficient Policy Evaluation
NeurIPS Workshop on Offline Reinforcement Learning (NeurIPS), 2021
Abstract | BibTex | arXiv | Code
NeurIPSdeep-rlpolicy-evaluation

Lukas Schäfer, Filippos Christianos, Josiah Hanna, Stefano V. Albrecht
Decoupling Exploration and Exploitation in Reinforcement Learning
ICML Workshop on Unsupervised Reinforcement Learning (ICML), 2021
Abstract | BibTex | arXiv | Code
ICMLdeep-rlintrinsic-reward

Trevor McInroe, Lukas Schäfer, Stefano V. Albrecht
Learning Temporally-Consistent Representations for Data-Efficient Reinforcement Learning
arXiv:2110.04935, 2021
Abstract | BibTex | arXiv | Code
deep-rl

2020

Filippos Christianos, Lukas Schäfer, Stefano V. Albrecht
Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
Conference on Neural Information Processing Systems (NeurIPS), 2020
Abstract | BibTex | arXiv
NeurIPSdeep-rlmulti-agent-rl

Georgios Papoudakis, Filippos Christianos , Lukas Schäfer, Stefano V. Albrecht
Comparative Evaluation of Multi-Agent Deep Reinforcement Learning Algorithms
arXiv:2006.07869, 2020
Abstract | BibTex | arXiv
deep-rlmulti-agent-rl